WidthEstimate2D

turbustat.statistics.WidthEstimate2D(inList, method='contour', noise_ACF=0, diagnosticplots=False, brunt_beamcorrect=True, beam_fwhm=None, spatial_cdelt=None, **fit_kwargs)

Estimate spatial widths from a set of autocorrelation images.

Warning

Error estimation is not implemented for interpolate or xinterpolate.

Parameters:

inList: {list of 2D `~numpy.ndarray`s, 3D `~numpy.ndarray}

The list of autocorrelation images.

method: {‘contour’, ‘fit’, ‘interpolate’, ‘xinterpolate’}, optional

The width estimation method to use. contour fits an ellipse to the 1/e contour about the peak. fit fits a 2D Gaussian to the peak. interpolate and xinterpolate both estimate the 1/e level from interpolating the data onto a finer grid near the center. xinterpolate first fits a 2D Gaussian to estimate the radial distances about the peak.

noise_ACF: {float, 2D `~numpy.ndarray`}, optional

The noise autocorrelation function to subtract from the autocorrelation images. This is typically produced by the last few eigenimages, whose structure should consistent of irreducible noise.

diagnosticplots: bool, optional

Show diagnostic plots for the first 9 autocorrelation images showing the goodness of fit (for the gaussian estimator) or ??? (presently nothing) for the others.

brunt_beamcorrectbool, optional

Apply the beam correction. When enabled, the beam size must be given.

beam_fwhmNone or astropy.units.Quantity

The FWHM beam width in angular units. Must be given when using brunt_beamcorrect.

spatial_cdelt{None, astropy.units.Quantity}, optional

The angular scale of a pixel in the given data. Must be given when using brunt_beamcorrect.

fit_kwargsdict, optional

Used when method is ‘contour’. Passed to turbustat.statistics.stats_utils.EllipseModel.estimate_stderrs.

Returns:

scalesarray

The array of estimated scales with length len(inList) or the 0th dimension size if inList is a 3D array.

scale_errorsarray

Uncertainty estimations on the scales.